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Comparative Analysis of Polycystic Ovary Syndrome Detection Using Machine Learning Algorithms

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  • @ARTICLE{10.4108/eetpht.10.5552,
        author={Neha Yadav and Ranjith Kumar A and Sagar Dhanraj Pande},
        title={Comparative Analysis of Polycystic Ovary Syndrome Detection Using Machine Learning Algorithms},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2024},
        month={3},
        keywords={Polycystic Ovary Syndrome, RSCV, GSCV, BO, Optuna, TPOT},
        doi={10.4108/eetpht.10.5552}
    }
    
  • Neha Yadav
    Ranjith Kumar A
    Sagar Dhanraj Pande
    Year: 2024
    Comparative Analysis of Polycystic Ovary Syndrome Detection Using Machine Learning Algorithms
    PHAT
    EAI
    DOI: 10.4108/eetpht.10.5552
Neha Yadav1, Ranjith Kumar A1, Sagar Dhanraj Pande2,*
  • 1: Lovely Professional University
  • 2: Pimpri Chinchwad University
*Contact email: sagarpande30@gmail.com

Abstract

INTRODUCTION: Polycystic Ovary Syndrome is a condition in which the ovaries manufacture androgen, seen in small traces, resulting in the production of cysts. Menstrual cycle abnormalities, clinical and/or biochemical hyperandrogenism, and the presence of polycystic ovaries on ultrasound should all be used to diagnose PCOS. PCOS appears to be a multifaceted illness influenced by both genetic and environmental factors and the symptoms include excessive hair on the face and body, weight gain, voice changes, skin type changes, and irregular periods. OBJECTIVES: This is the objective of this paper is to identify PCOS in its initial stage. METHODS: To address this issue the study proposes a comparison of various machine learning algorithms and optimization techniques Among which GSCV gave the best result of 94% accuracy, followed by TPOT with 91% accuracy. Additionally, we also applied Feature selection methods to eliminate zero-importance features to increase the accuracy of algorithms. RESULTS: The main results obtained in this paper This study explored various Feature selection techniques, ML and DL models. It is shown that Grid Search CV and TPOT classifier were best classifiers with 94% and 91% respectively. CONCLUSION: These are the conclusions of this paper and this study will explore various DL methodologies and try to find out best optimal results for the PCOS Detection. And also, to develop an PCOS detection application to keep track of menstrual cycles and track activities and symptoms for PCOS. 

Keywords
Polycystic Ovary Syndrome, RSCV, GSCV, BO, Optuna, TPOT
Received
2023-12-18
Accepted
2024-03-18
Published
2024-03-26
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.10.5552

Copyright © 2024 N. Yadav et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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